Real-time object tracking based on an adaptive transition model and extended Kalman filter to handle full occlusion

Abstract

In this paper, a tracker scheme is proposed that not only can face object tracking challenges but also can estimate object positions over occluded frames. In the proposed scheme, kernelized correlation filter (KCF) is considered as our basic tracker due to its high efficiency in the most object tracking challenges except occlusion and illumination variation. To improve the efficiency of KCF, the proposed method integrates an occlusion detection method, an adaptive model update and a prediction system into the KCF tracker. The occlusion detection method is based on the peak-to-sidelobe ratio of the confidence map to determine the type of occlusion. When an object is partially occluded, the object appearance model is adaptively updated to increase the accuracy of object tracking. When full occlusion occurs, the proposed predictor is run and exploits the available motion information before the occurrence of full occlusion to predict the location of the tracked object. The proposed predictor uses adaptive transition state equations to estimate the acceleration and velocity of the object needed in the extended Kalman filter (EKF) to predict object position. It also uses two quadratic equations to estimate the object trajectory. Finally, a method is proposed that exploits the estimated object positions by both EKF and the object trajectory to predict object positions over fully occluded frames. Experimental results on open datasets show that the proposed method achieved a better performance in comparison with several state-of-the-art trackers.